Hunters and Trappers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Hunters and Trappers (ISCO-08 6224) score an average of 0.09 on a 0–1 exposure scale — more exposed than about 1% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Not exposed band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Repairing and maintaining equipment.”

Scores 0.14 on the 2025 scale. The task of "Repairing and maintaining equipment" involves physical interaction, hands-on mechanical skills, troubleshooting, and decision-making based on real-time assessments, which are beyond the current capabilities of Generative AI. Similar tasks, such as "Maintaining the serviced machine, removing minor faults" and "Performing minor repairs and maintenance tasks on roads, buildings, facilities, and forestry equipment," have received low automation scores (0.15 and 0.11, respectively) due to their physical nature and the need for human dexterity and expertise. While Generative AI can assist with diagnostics by analyzing data and suggesting maintenance procedures, the execution of physical repairs still necessitates human intervention. Considering the technology access in a high-income country like Poland, AI can support maintenance tasks by providing guidance or instructions, but it cannot automate the core physical activities. Therefore, the adjusted score reflects the limited potential for automation in this context, acknowledging AI's auxiliary role but recognizing the indispensability of human skills for this task.

Moving fastest, 2023 → 2025

“Repairing and maintaining equipment.”

Model capability on this task changed by +0.04 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 6224, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• First-Line Supervisors of Farming, Fishing, and Forestry Workers

In context

Part of the 6 - Skilled agricultural, forestry and fishery workers major group. Return to the full gradient to see how the whole group sits.

Hunters and Trappers sit at the 1st percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Hunters and Trappers rank in the 1st percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure fell by 0.00 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Repairing and maintaining equipment.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Hunters and Trappers". https://singulariki.com/gradient/6224-hunters-and-trappers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.